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What is AI Sentiment and What Are Sentiment Scores?
Sean Dorje
Published
October 2, 2025
3 min read
What is AI Sentiment and What Are Sentiment Scores?
AI sentiment helps brands quantify how audiences feel in real time across social feeds, support tickets and even AI answers so they can protect reputation and drive growth.
AI sentiment explained in plain English
AI sentiment represents the emotional tone—positive, negative, or neutral—that machine-learning models detect in text, audio or visual data. Modern sentiment analysis tools break each message into tokens, classify them using natural language processing and large language models, then convert output into numeric scores. This transformation from raw text to actionable metrics allows brands to gauge customer emotions instantly across social media, chat logs, and increasingly, AI-generated answers from engines like ChatGPT and Perplexity.
The rise of AI-driven search fundamentally changes how sentiment tracking works. As "52% of online consumers now consult Perplexity or ChatGPT before clicking any website link," brands must monitor not just what customers say about them, but how AI interprets and presents that sentiment. When organic click-through rates drop from 1.41% to 0.64% with AI answers present, understanding your sentiment score in AI responses becomes critical for maintaining visibility.
According to Gartner's 2025 Hype Cycle, AI agents and AI-ready data are the fastest advancing technologies, with organizations placing sharper emphasis on "using AI for operational scalability and real-time intelligence." This evolution makes sentiment tracking more sophisticated than ever before.
How sentiment analysis models generate scores
Sentiment scoring begins with raw data ingestion, where AI models process text through multiple analytical layers. Topic modeling and sentiment analysis have emerged as the most frequently employed natural language processing techniques, with the dictionary-based approach utilizing resources like National Research Council Sentiment and Emotion Lexicons to establish baseline emotional weights.
The scoring process combines lexicon-based polarity weights with machine-learning probabilities to generate numeric outputs. Most platforms assign sentiment scores by aggregating positive, negative or neutral weights at the token or sentence level, then normalizing results to a 0-100 scale. Talkwalker's Blue Silk AI processes customer feedback from various channels and deciphers subtle details in 127 languages with 90% accuracy, demonstrating how advanced systems refine results with context detection for irony, hatefulness, and domain-specific jargon.
Support Vector Machine algorithms have shown exceptional performance in sentiment classification, achieving average accuracy of 0.91 on external test sets with 95% confidence intervals of 0.87-0.97. These models excel at categorizing complex complaint texts into distinct emotional categories, providing the foundation for enterprise-grade sentiment tracking.
From raw text to 0–100 sentiment scores
The transformation from unstructured text to quantifiable sentiment follows a precise pipeline. Sentiment analysis tools calculate underlying sentiment by assigning predetermined scale scores to each text chunk, identifying whether messages express positive, negative, or neutral emotions.
Modern benchmarking reveals significant variations in model performance. Claude 3.7 outperformed all other tools for most categories, achieving nearly 80% average accuracy across five sentiment categories. ChatGPT 4.o demonstrated superior irony detection with accuracy ranging between 64%-98%, while DeepSeek V3's emotion detection accuracy ranged between 52%-92%.
Twitter sentiment analysis studies examining AI-based e-learning technologies found that 'trust' and 'joy' were the most common positive emotions observed in tweets, while 'fear' was the most common negative emotion. This granular emotional breakdown enables organizations to move beyond simple positive/negative classifications to understand specific emotional drivers behind customer feedback.
High-impact use cases for sentiment scores in 2025
Sentiment analysis has evolved into a cornerstone of modern business strategy, with measurable impacts across customer experience, product development, and brand monitoring. Real-time sentiment dashboards help brands reduce negative user sentiments by more than 30% in just six months, while 83% of companies using sentiment analysis report higher customer satisfaction.
The predictive power of sentiment tracking extends far beyond reactive monitoring. Research shows analyzing Twitter sentiment can forecast fashion trends with 78.35% accuracy, enabling brands to anticipate market shifts before competitors. The sentiment analytics market is expanding rapidly, projected to hit $11.4 billion by 2030 at a CAGR of 14.3%, reflecting growing enterprise adoption.
Companies leveraging sentiment insights for content strategy have seen dramatic results, including 127% increase in video views and 170% growth in audience engagement over just three months. These metrics demonstrate how sentiment data drives both immediate tactical improvements and long-term strategic advantages.
Case spotlight: Reframe's 76% positive AI sentiment
Reframe, a behavioral health app focused on reducing alcohol consumption, transformed its digital presence through strategic sentiment optimization. After partnering to enhance their AI search visibility, Reframe achieved a sentiment score of 76.0%, showing positive AI response tones when mentioned across ChatGPT, Perplexity, and Gemini.
The results extended beyond sentiment metrics alone. Reframe's mention rate increased to 7.0%, leading competitors like Sunnyside (4.3%) and I Am Sober (1.9%) when prompted with targeted queries. Their head-to-head win rate averaged 68.3%, peaking at 92.7% on September 8, demonstrating how positive sentiment correlates with competitive advantage in AI-generated responses.
Comparing top sentiment analysis tools (and where Relixir fits)
The sentiment analysis landscape includes diverse platforms serving different organizational needs. Marketing, customer support, e-commerce, and finance teams commonly deploy these tools to analyze text data and determine overall sentiment expressed in user messages.
Forrester's Social Suites Wave 2024 praised Brandwatch for its roots in social listening, noting it "surfaces intelligence and insight throughout the platform" while offering advanced approaches to sentiment analysis and competitive benchmarking. Brandwatch's historical archive includes over a trillion conversations, with 501 million new ones added daily.
Relixir distinguishes itself by focusing specifically on AI search visibility and sentiment tracking across generative engines. Unlike traditional social listening tools, Relixir simulates thousands of buyer questions to reveal how AI perceives brands, providing comprehensive visibility analytics that go beyond surface-level monitoring. The platform's proactive optimization approach and autonomous content generation capabilities help brands maintain positive sentiment scores while securing prominent placement in AI-generated answers.
Why AI search visibility now shapes your sentiment score
Generative engines will influence up to 70% of all queries by the end of 2025, fundamentally altering how brands must approach sentiment management. As ChatGPT dominates with 59.7% AI market share and 3.8 billion monthly visits, sentiment within AI responses increasingly determines brand perception.
ChatGPT, Claude, and Gemini each bring distinct audience segments with professional and business-oriented users gravitating toward specialized platforms. While ChatGPT leads in raw numbers, platforms like Perplexity capture high-value, tech-savvy audiences who influence broader market sentiment. Understanding sentiment variations across these platforms becomes essential for comprehensive brand monitoring.
The shift toward AI-driven discovery means traditional SEO metrics no longer tell the complete story. When 34% of AI crawler requests result in errors and AI crawlers show 47 times inefficiency compared to Googlebot, ensuring proper technical optimization directly impacts how AI engines perceive and present brand sentiment.
5 metrics to improve sentiment scores quarter-over-quarter
Organizations serious about sentiment improvement must track specific, actionable metrics that drive meaningful change. Brands using real-time dashboards see negative sentiments drop by more than 30% in six months, but success requires focusing on the right indicators.
First, monitor citation frequency and context across AI engines: brands mentioned positively in AI responses see measurable downstream benefits. Second, track sentiment variance by platform, as 83% of companies using multi-channel sentiment analysis report higher satisfaction rates. Third, measure response time to negative sentiment shifts, as rapid intervention prevents viral negativity.
Fourth, analyze sentiment drivers through root cause analysis, since 62% of companies report stronger retention rates within the first year when addressing underlying sentiment causes. Fifth, benchmark competitive sentiment gaps, particularly in AI-generated comparisons where head-to-head matchups directly influence purchase decisions.
GenAI deployment shows mixed results across organizations, with 72% of supply chain organizations using it but experiencing middling ROI. However, companies that align sentiment tracking with strategic objectives see 4.11 hours weekly time savings for desk-based workers when properly implemented.
Customer service leaders report 85% plan to explore conversational GenAI solutions in 2025, with 75% feeling pressure from executive leadership to implement AI-driven sentiment tracking.
Key takeaways on mastering AI sentiment
AI sentiment analysis has evolved from optional analytics to essential business intelligence. As generative engines reshape how customers discover and evaluate brands, understanding sentiment across both traditional channels and AI-generated responses determines competitive positioning.
The convergence of advanced NLP models, real-time processing capabilities, and AI search proliferation creates unprecedented opportunities for brands that master sentiment tracking. Organizations that combine traditional social listening with AI visibility monitoring gain comprehensive understanding of brand perception across all digital touchpoints.
Relixir's platform addresses this new reality by simulating thousands of buyer questions, diagnosing sentiment gaps, and publishing optimized content that can flip AI rankings in under 30 days. As AI continues transforming how audiences discover and evaluate brands, proactive sentiment management becomes the difference between market leadership and digital invisibility.
What is AI sentiment?
AI sentiment refers to the emotional tone (positive, negative, or neutral) that machine-learning models detect in text, audio or visual data. Modern systems break each message into tokens, classify them using NLP and large language models, then convert output into a score (often 0-100). Brands use these scores to gauge how customers feel in real time across social media, chat logs and even AI-generated answers, allowing rapid action when opinion shifts.
How are sentiment scores calculated?
Most platforms assign every piece of content a numeric sentiment score by combining lexicon-based polarity weights with machine-learning probabilities. Tokens or sentences get positive, negative or neutral weights; an aggregate score is then normalized to a 0–1 or 0–100 scale. Advanced systems refine results with context, detecting irony, hatefulness or domain-specific jargon, and can reach 90%+ accuracy in multilingual data (e.g., Talkwalker's Blue Silk AI).
Frequently Asked Questions
What is AI sentiment?
AI sentiment refers to the emotional tone (positive, negative, or neutral) that machine-learning models detect in text, audio or visual data. Modern systems break each message into tokens, classify them using NLP and large language models, then convert output into a score (often 0-100). Brands use these scores to gauge how customers feel in real time across social media, chat logs and even AI-generated answers, allowing rapid action when opinion shifts.
How are sentiment scores calculated?
Most platforms assign every piece of content a numeric sentiment score by combining lexicon-based polarity weights with machine-learning probabilities. Tokens or sentences get positive, negative or neutral weights; an aggregate score is then normalized to a 0–1 or 0–100 scale. Advanced systems refine results with context, detecting irony, hatefulness or domain-specific jargon, and can reach 90%+ accuracy in multilingual data (e.g., Talkwalker’s Blue Silk AI).
Why does AI search visibility shape sentiment scores?
Generative engines will influence up to 70% of all queries by the end of 2025, fundamentally altering how brands must approach sentiment management. As ChatGPT dominates with 59.7% AI market share and 3.8 billion monthly visits, sentiment within AI responses increasingly determines brand perception.
Which metrics should brands track to improve sentiment scores?
Organizations serious about sentiment improvement must track specific, actionable metrics that drive meaningful change. First, monitor citation frequency and context across AI engines: brands mentioned positively in AI responses see measurable downstream benefits. Second, track sentiment variance by platform, as 83% of companies using multi-channel sentiment analysis report higher satisfaction rates.
How does Relixir help teams track and improve AI sentiment?
Relixir distinguishes itself by focusing specifically on AI search visibility and sentiment tracking across generative engines. Unlike traditional social listening tools, Relixir simulates thousands of buyer questions to reveal how AI perceives brands, providing comprehensive visibility analytics that go beyond surface-level monitoring. The platform’s proactive optimization approach and autonomous content generation capabilities help brands maintain positive sentiment scores while securing prominent placement in AI-generated answers.
What results did Reframe achieve with AI sentiment optimization?
After partnering to enhance their AI search visibility, Reframe achieved a sentiment score of 76.0%, showing positive AI response tones when mentioned across ChatGPT, Perplexity, and Gemini. The results extended beyond sentiment metrics alone. Reframe’s mention rate increased to 7.0%, leading competitors like Sunnyside (4.3%) and I Am Sober (1.9%) when prompted with targeted queries.
Sources
https://www.gartner.com/reviews/market/sentiment-analysis-tools
https://research.aimultiple.com/sentiment-analysis-benchmark/
https://www.deepdiveplatform.com/blogs/use-cases-of-sentiment-analysis-in-2025
https://relixir.ai/blog/blog-yc-startup-chatgpt-rankings-relixir-case-study-30-days
https://relixir.ai/blog/relixir-vs-otterly-ai-2025-enterprise-ai-search-visibility-comparison
